A history of water distribution systems and their optimisation
- Authors: Mala-Jetmarova, Helena , Barton, Andrew , Bagirov, Adil
- Date: 2015
- Type: Text , Journal article
- Relation: Water Science and Technology-Water Supply Vol. 15, no. 2 (2015), p. 224-235
- Relation: http://purl.org/au-research/grants/arc/LP0990908
- Full Text: false
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- Description: Water distribution systems have a very long and rich history dating back to the third millennium B.C. Advances in water supply and distribution were followed in parallel by discoveries and inventions in other related fields. Therefore, it is the aim of this paper to review both the history of water distribution systems and those related fields in order to present a coherent summary of the complex multi-stranded discipline of water engineering. Related fields reviewed in this paper include devices for raising water and water pumps, water quality and water treatment, hydraulics, network analysis, and optimisation of water distribution systems. The review is brief and concise and allows the reader to quickly gain an understanding of the history and advancements of water distribution systems and analysis. Furthermore, the paper gives details of other existing publications where more information can be found.
Application of optimisation-based data mining techniques to medical data sets: A comparative analysis
- Authors: Dzalilov, Zari , Bagirov, Adil , Mammadov, Musa
- Date: 2012
- Type: Text , Conference paper
- Relation: IMMM 2102: The Second International Conference on Advances in Information Mining and Management p. 41-46
- Full Text: false
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- Description: Abstract - Computational methods have become an important tool in the analysis of medical data sets. In this paper, we apply three optimisation-based data mining methods to the following data sets: (i) a cystic fibrosis data set and (ii) a tobacco control data set. Three algorithms used in the analysis of these data sets include: the modified linear least square fit, an optimization based heuristic algorithm for feature selection and an optimization based clustering algorithm. All these methods explore the relationship between features and classes, with the aim of determining contribution of specific features to the class outcome. However, the three algorithms are based on completely different approaches. We apply these methods to solve feature selection and classification problems. We also present comparative analysis of the algorithms using computational results. Results obtained confirm that these algorithms may be effectively applied to the analysis of other (bio)medical data sets
Feature selection using misclassification counts
- Authors: Bagirov, Adil , Yatsko, Andrew , Stranieri, Andrew
- Date: 2011
- Type: Conference proceedings , Unpublished work
- Relation: Proceedings of the 9th Australasian Data Mining Conference (AusDM 2011), 51-62. Conferences in Research and Practice in Information Technology (CRPIT), Vol. 121.
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- Description: Dimensionality reduction of the problem space through detection and removal of variables, contributing little or not at all to classification, is able to relieve the computational load and instance acquisition effort, considering all the data attributes accessed each time around. The approach to feature selection in this paper is based on the concept of coherent accumulation of data about class centers with respect to coordinates of informative features. Ranking is done on the degree to which different variables exhibit random characteristics. The results are being verified using the Nearest Neighbor classifier. This also helps to address the feature irrelevance and redundancy, what ranking does not immediately decide. Additionally, feature ranking methods from different independent sources are called in for the direct comparison.
- Description: Dimensionality reduction of the problem space through detection and removal of variables, contributing little or not at all to classification, is able to relieve the computational load and the data acquisition effort, considering all data components being accessed each time around. The approach to feature selection in this paper is based on the concept of coherent accumulation of data about class centers with respect to coordinates of informative features. Ranking is done on the degree, to which different variables exhibit random characteristics. The results are being verified using the Nearest Neighbor classifier. This also helps to address the feature irrelevance, what ranking does not immediately decide. Additionally, feature ranking methods available from different independent sources are called in for direct comparison.
Adaption to water shortage through the implementation of a unique pipeline system in Victoria, Australia
- Authors: Mala-Jetmarova, Helena , Barton, Andrew , Bagirov, Adil , McRae-Williams, Pamela , Caris, Rob , Jackson, Peter
- Date: 2010
- Type: Conference paper
- Relation: Paper presented at Hydropredict' 2010, 2nd International Interdisciplinary Conference on predications for Hydrology, Ecology, and Water Resources Management
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- Description: Abstract Water resource development has played a crucial role in the Grampians, Wimmera and Mallee regions of Australia, with the main source of surface water located in several reservoirs in the Grampians mountain ranges. Historically, water was delivered by gravity through a vast 19 500 km earthen channel system from the reservoirs to the townships and farms. As a result of the severe and protracted drought experienced in the region over the past 13 years and the projected drying climate, there have been fundamental changes made to the management of water in order to better cope with water scarcity. The primary strategic effort to sustainably manage water resources was by removing the unsustainable transport of water via the open channels which resulted in very high losses through seepage and evaporation. This inefficient system has been replaced by a pressurised pipeline, the largest geographical water infrastructure project of its type in Australia, spreading across an area of approximately 20 000 km2. To manage the change in water balance as a result of the pipeline and drying climate, the regions water corporations and environmental agencies have designed a scheme for water allocations intended to sustain local communities, allow for regional development and improve environmental conditions. This paper describes the unique pipeline system recently completed, provides a brief summary of water sharing arrangements and introduces the research program currently underway to optimise the performance of the pipeline system.
An approximate subgradient algorithm for unconstrained nonsmooth, nonconvex optimization
- Authors: Bagirov, Adil , Ganjehlou, Asef Nazari
- Date: 2008
- Type: Text , Journal article
- Relation: Mathematical Methods of Operations Research Vol. 67, no. 2 (2008), p. 187-206
- Relation: http://purl.org/au-research/grants/arc/DP0666061
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- Description: In this paper a new algorithm for minimizing locally Lipschitz functions is developed. Descent directions in this algorithm are computed by solving a system of linear inequalities. The convergence of the algorithm is proved for quasidifferentiable semismooth functions. We present the results of numerical experiments with both regular and nonregular objective functions. We also compare the proposed algorithm with two different versions of the subgradient method using the results of numerical experiments. These results demonstrate the superiority of the proposed algorithm over the subgradient method. © 2007 Springer-Verlag.
- Description: C1
Derivative free stochastic discrete gradient method with adaptive mutation
- Authors: Ghosh, Ranadhir , Ghosh, Moumita , Bagirov, Adil
- Date: 2006
- Type: Text , Journal article
- Relation: Advances in Data Mining Vol. 4065, no. (2006), p. 264-278
- Full Text: false
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- Description: In data mining we come across many problems such as function optimization problem or parameter estimation problem for classifiers for which a good learning algorithm for searching is very much necessary. In this paper we propose a stochastic based derivative free algorithm for unconstrained optimization problem. Many derivative-based local search methods exist which usually stuck into local solution for non-convex optimization problems. On the other hand global search methods are very time consuming and works for only limited number of variables. In this paper we investigate a derivative free multi search gradient based method which overcomes the problems of local minima and produces global solution in less time. We have tested the proposed method on many benchmark dataset in literature and compared the results with other existing algorithms. The results are very promising.
- Description: C1
- Description: 2003001541
An algorithm for minimizing clustering functions
- Authors: Bagirov, Adil , Ugon, Julien
- Date: 2005
- Type: Text , Journal article
- Relation: Optimization Vol. 54, no. 4-5 (Aug-Oct 2005), p. 351-368
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- Description: The problem of cluster analysis is formulated as a problem of nonsmooth, nonconvex optimization. An algorithm for solving the latter optimization problem is developed which allows one to significantly reduce the computational efforts. This algorithm is based on the so-called discrete gradient method. Results of numerical experiments are presented which demonstrate the effectiveness of the proposed algorithm.
- Description: C1
- Description: 2003001266
Data mining with combined use of optimization techniques and self-organizing maps for improving risk grouping rules : Application to prostate cancer patients
- Authors: Churilov, Leonid , Bagirov, Adil , Schwartz, Daniel , Smith, Kate , Dally, Michael
- Date: 2005
- Type: Text , Journal article
- Relation: Journal of Management Information Systems Vol. 21, no. 4 (2005), p. 85-100
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- Description: Data mining techniques provide a popular and powerful tool set to generate various data-driven classification systems. In this paper, we investigate the combined use of self-organizing maps (SOM) and nonsmooth nonconvex optimization techniques in order to produce a working case of a data-driven risk classification system. The optimization approach strengthens the validity of SOM results, and the improved classification system increases both the quality of prediction and the homogeneity within the risk groups. Accurate classification of prostate cancer patients into risk groups is important to assist in the identification of appropriate treatment paths. We start with the existing rules and aim to improve classification accuracy by identifying inconsistencies utilizing self-organizing maps as a data visualization tool. Then, we progress to the study of assigning prostate cancer patients into homogenous groups with the aim to support future clinical treatment decisions. Using the case of prostate cancer patients grouping, we demonstrate strong potential of data-driven risk classification schemes for addressing the risk grouping issues in more general organizational settings. © 2005 M.E. Sharpe, Inc.
- Description: C1
- Description: 2003001265
Optimization of feed forward MLPs using the discrete gradient method
- Authors: Bagirov, Adil , Yearwood, John , Ghosh, Ranadhir
- Date: 2004
- Type: Text , Conference paper
- Relation: Paper presented at CIMCA 2004: International Conference on Computational Intelligence for Modelling, Control & Automation, Gold Coast, Queensland : 12th July, 2004
- Full Text: false
- Reviewed:
- Description: E1
- Description: 2003000845
An optimization-based approach to patient grouping for acute healthcare in Australia
- Authors: Bagirov, Adil , Churilov, Leonid
- Date: 2003
- Type: Text , Conference paper
- Relation: Paper presented at Computational Science - ICCS 2003 Conference, Melbourne : 2nd June, 2003
- Full Text: false
- Reviewed:
- Description: The problem of cluster analysis is formulated as a problem of nonsmooth, nonconvex optimization, and an algorithm for solving the cluster analysis problem based on the nonsmooth optimization techniques is developed. The issues of applying this algorithm to large data sets are discussed and a feature selection procedure is demonstrated. The algorithm is then applied to a hospital data set to generate new knowledge about different patterns of patients resource consumption.
- Description: E1
- Description: 2003000434
Lagrange-type functions in constrained optimization
- Authors: Rubinov, Alex , Yang, Xiao , Bagirov, Adil , Gasimov, Rafail
- Date: 2003
- Type: Text , Journal article
- Relation: Journal of Mathematical Sciences Vol. 115, no. 4 (2003), p. 2437-2505
- Full Text: false
- Reviewed:
- Description: We examine various kinds of nonlinear Lagrange-type functions for constrained optimization problems. In particular, we study the weak duality, the zero duality gap property, and the existence of an exact parameter for these functions. The paper contains a detailed survey of results in these directions and comparison of different methods proposed by different authors. Some new results are also given.
- Description: C1
- Description: 2003000358
Parallelization of the discrete gradient method of non-smooth optimization and its applications
- Authors: Beliakov, Gleb , Tobon, Monsalve , Bagirov, Adil
- Date: 2003
- Type: Text , Conference paper
- Relation: Paper presented at Computational Science ICCS 2003 Conference, Melbourne : 2nd June, 2003
- Full Text: false
- Reviewed:
- Description: We investigate parallelization and performance of the discrete gradient method of nonsmooth optimization. This derivative free method is shown to be an effective optimization tool, able to skip many shallow local minima of nonconvex nondifferentiable objective functions. Although this is a sequential iterative method, we were able to parallelize critical steps of the algorithm, and this lead to a significant improvement in performance on multiprocessor computer clusters. We applied this method to a difficult polyatomic clusters problem in computational chemistry, and found this method to outperform other algorithms.
- Description: E1
- Description: 2003000435
Penalty functions with a small penalty parameter : Numerical experiments
- Authors: Bagirov, Adil , Rubinov, Alex
- Date: 2003
- Type: Text , Conference paper
- Relation: Paper presented at Industrial Optimization Conference 2003, Perth : 30th September, 2002
- Full Text: false
- Reviewed:
- Description: E1
- Description: 2003000432
The discrete gradient evolutionary strategy method for global optimization
- Authors: Abbas, Hussein , Bagirov, Adil , Zhang, Jiapu
- Date: 2003
- Type: Text , Conference paper
- Relation: Paper presented at the Congress on Evolutionary Computation CEC 2003, Canberra : 8th December, 2003
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- Reviewed:
- Description: Global optimization problems continue to be a challenge in computational mathematics. The field is progressing in two streams: deterministic and heuristic approaches. In this paper, we present a hybrid method that uses the discrete gradient method, which is a derivative free local search method, and evolutionary strategies. We show that the hybridization of the two methods is better than each of them in isolation.
- Description: E1
- Description: 2003000440
Unsupervised and supervised data classification via nonsmooth and global optimisation
- Authors: Bagirov, Adil , Rubinov, Alex , Sukhorukova, Nadezda , Yearwood, John
- Date: 2003
- Type: Text , Journal article
- Relation: Top Vol. 11, no. 1 (2003), p. 1-92
- Full Text:
- Reviewed:
- Description: We examine various methods for data clustering and data classification that are based on the minimization of the so-called cluster function and its modications. These functions are nonsmooth and nonconvex. We use Discrete Gradient methods for their local minimization. We consider also a combination of this method with the cutting angle method for global minimization. We present and discuss results of numerical experiments.
- Description: C1
- Description: 2003000421
Penalty functions with a small penalty parameter
- Authors: Rubinov, Alex , Yang, Xiao , Bagirov, Adil
- Date: 2002
- Type: Text , Journal article
- Relation: Optimization Methods and Software Vol. 17, no. 5 (2002), p. 931-964
- Full Text: false
- Reviewed:
- Description: In this article, we study the nonlinear penalization of a constrained optimization problem and show that the least exact penalty parameter of an equivalent parametric optimization problem can be diminished. We apply the theory of increasing positively homogeneous (IPH) functions so as to derive a simple formula for computing the least exact penalty parameter for the classical penalty function through perturbation function. We establish that various equivalent parametric reformulations of constrained optimization problems lead to reduction of exact penalty parameters. To construct a Lipschitz penalty function with a small exact penalty parameter for a Lipschitz programming problem, we make a transformation to the objective function by virtue of an increasing concave function. We present results of numerical experiments, which demonstrate that the Lipschitz penalty function with a small penalty parameter is more suitable for solving some nonconvex constrained problems than the classical penalty function.
- Description: 2003000116
Global optimization of marginal functions with applications to economic equilibrium
- Authors: Bagirov, Adil , Rubinov, Alex
- Date: 2001
- Type: Text , Journal article
- Relation: Journal of Global Optimization Vol. 20, no. 3-4 (Aug 2001), p. 215-237
- Full Text: false
- Reviewed:
- Description: We discuss the applicability of the cutting angle method to global minimization of marginal functions. The search of equilibrium prices in the exchange model can be reduced to the global minimization of certain functions, which include marginal functions. This problem has been approximately solved by the cutting angle method. Results of numerical experiments are presented and discussed.